Multichannel Convolutional Text Classification.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"CNN Multichannel Text Classification.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"metadata":{"id":"1Xc8EKCGWo_V","colab_type":"code","outputId":"d17c4acb-0f9a-4f9d-fc81-b875aee410bd","executionInfo":{"status":"ok","timestamp":1546011501065,"user_tz":-330,"elapsed":85946,"user":{"displayName":"Gaurav Patel","photoUrl":"https://lh3.googleusercontent.com/-OePEQgv0Daw/AAAAAAAAAAI/AAAAAAAAAaI/dyKmC54H6hc/s64/photo.jpg","userId":"02723724286804741615"}},"colab":{"base_uri":"https://localhost:8080/","height":1079}},"cell_type":"code","source":["!pip install torch\n","!pip install torchtext\n","!pip install -U spacy\n","!python -m spacy download en\n","!pip install nltk"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Collecting torch\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7e/60/66415660aa46b23b5e1b72bc762e816736ce8d7260213e22365af51e8f9c/torch-1.0.0-cp36-cp36m-manylinux1_x86_64.whl (591.8MB)\n","\u001b[K    100% |████████████████████████████████| 591.8MB 26kB/s \n","tcmalloc: large alloc 1073750016 bytes == 0x620be000 @  0x7ff203a3e2a4 0x591a07 0x5b5d56 0x502e9a 0x506859 0x502209 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x507641 0x502209 0x502f3d 0x506859 0x504c28 0x502540 0x502f3d 0x507641 0x504c28 0x502540 0x502f3d 0x507641\n","\u001b[?25hInstalling collected packages: torch\n","Successfully installed torch-1.0.0\n","Collecting torchtext\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c6/bc/b28b9efb4653c03e597ed207264eea45862b5260f48e9f010b5068d64db1/torchtext-0.3.1-py3-none-any.whl (62kB)\n","\u001b[K    100% |████████████████████████████████| 71kB 3.3MB/s \n","\u001b[?25hRequirement already satisfied: tqdm in 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torchtext-0.3.1\n","Requirement already up-to-date: spacy in /usr/local/lib/python3.6/dist-packages (2.0.18)\n","Requirement already satisfied, skipping upgrade: preshed<2.1.0,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from spacy) (2.0.1)\n","Requirement already satisfied, skipping upgrade: thinc<6.13.0,>=6.12.1 in /usr/local/lib/python3.6/dist-packages (from spacy) (6.12.1)\n","Collecting numpy>=1.15.0 (from spacy)\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ff/7f/9d804d2348471c67a7d8b5f84f9bc59fd1cefa148986f2b74552f8573555/numpy-1.15.4-cp36-cp36m-manylinux1_x86_64.whl (13.9MB)\n","\u001b[K    100% |████████████████████████████████| 13.9MB 3.0MB/s \n","\u001b[?25hRequirement already satisfied, skipping upgrade: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (2.18.4)\n","Requirement already satisfied, skipping upgrade: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.0.1)\n","Requirement already satisfied, skipping upgrade: dill<0.3,>=0.2 in /usr/local/lib/python3.6/dist-packages (from spacy) (0.2.8.2)\n","Requirement already satisfied, skipping upgrade: ujson>=1.35 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.35)\n","Requirement already satisfied, skipping upgrade: regex==2018.01.10 in /usr/local/lib/python3.6/dist-packages (from spacy) (2018.1.10)\n","Requirement already satisfied, skipping upgrade: plac<1.0.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy) (0.9.6)\n","Requirement already satisfied, skipping upgrade: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy) (2.0.2)\n","Requirement already satisfied, skipping upgrade: msgpack<0.6.0,>=0.5.6 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy) (0.5.6)\n","Requirement already satisfied, skipping upgrade: cytoolz<0.10,>=0.9.0 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy) (0.9.0.1)\n","Requirement already satisfied, skipping upgrade: tqdm<5.0.0,>=4.10.0 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy) (4.28.1)\n","Requirement already satisfied, skipping upgrade: wrapt<1.11.0,>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy) (1.10.11)\n","Requirement already satisfied, skipping upgrade: six<2.0.0,>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy) (1.11.0)\n","Requirement already satisfied, skipping upgrade: msgpack-numpy<0.4.4 in /usr/local/lib/python3.6/dist-packages (from thinc<6.13.0,>=6.12.1->spacy) (0.4.3.2)\n","Requirement already satisfied, skipping upgrade: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (1.22)\n","Requirement already satisfied, skipping upgrade: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2.6)\n","Requirement already satisfied, skipping upgrade: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (3.0.4)\n","Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2018.11.29)\n","Requirement already satisfied, skipping upgrade: toolz>=0.8.0 in /usr/local/lib/python3.6/dist-packages (from cytoolz<0.10,>=0.9.0->thinc<6.13.0,>=6.12.1->spacy) (0.9.0)\n","\u001b[31mfeaturetools 0.4.1 has requirement pandas>=0.23.0, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n","\u001b[31mcufflinks 0.14.6 has requirement plotly>=3.0.0, but you'll have plotly 1.12.12 which is incompatible.\u001b[0m\n","Installing collected packages: numpy\n","  Found existing installation: numpy 1.14.6\n","    Uninstalling numpy-1.14.6:\n","      Successfully uninstalled numpy-1.14.6\n","Successfully installed numpy-1.15.4\n","Requirement already satisfied: en_core_web_sm==2.0.0 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz#egg=en_core_web_sm==2.0.0 in /usr/local/lib/python3.6/dist-packages (2.0.0)\n","\n","\u001b[93m    Linking successful\u001b[0m\n","    /usr/local/lib/python3.6/dist-packages/en_core_web_sm -->\n","    /usr/local/lib/python3.6/dist-packages/spacy/data/en\n","\n","    You can now load the model via spacy.load('en')\n","\n","Requirement already satisfied: nltk in /usr/local/lib/python3.6/dist-packages (3.2.5)\n","Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from nltk) (1.11.0)\n"],"name":"stdout"}]},{"metadata":{"id":"EopWaTjVW2lo","colab_type":"code","colab":{}},"cell_type":"code","source":["import torch\n","from torchtext import data\n","from torchtext import datasets\n","import random\n","import nltk\n","import re\n","import numpy as np\n","from tqdm import tqdm\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","%matplotlib inline"],"execution_count":0,"outputs":[]},{"metadata":{"id":"LHeE87yJXbq_","colab_type":"code","colab":{}},"cell_type":"code","source":["SEED = 10\n","torch.manual_seed(SEED)\n","torch.cuda.manual_seed(SEED)\n","torch.backends.cudnn.deterministic = True #for reproducible results"],"execution_count":0,"outputs":[]},{"metadata":{"id":"7It70WIDXf3X","colab_type":"code","colab":{}},"cell_type":"code","source":["def clean_text(text):\n","    text = text.lower()\n","    text = re.compile('<.*?>').sub(\" \", text)\n","    text = re.sub(r\"i'm\", \"i am\", text)\n","    text = re.sub(r\"he's\", \"he is\", text)\n","    text = re.sub(r\"she's\", \"she is\", text)\n","    text = re.sub(r\"that's\", \"that is\", text)\n","    text = re.sub(r\"what's\", \"what is\", text)\n","    text = re.sub(r\"where's\", \"where is\", text)\n","    text = re.sub(r\"\\'ll\", \" will\", text)\n","    text = re.sub(r\"\\'ve\", \" have\", text)\n","    text = re.sub(r\"\\'re\", \" are\", text)\n","    text = re.sub(r\"\\'d\", \" would\", text)\n","    text = re.sub(r\"won't\", \"will not\", text)\n","    text = re.sub(r\"can't\", \"cannot\", text)\n","    text = re.sub(r\"[-()\\\"#/@;:<>{}+=~|.?,]\", \"\", text)\n","    text = text.split()\n","    return text\n","\n","TEXT = data.Field(tokenize=clean_text,lower=True, fix_length=800)\n","LABEL = data.LabelField(dtype = torch.float)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"hetNKN7xXicX","colab_type":"code","colab":{}},"cell_type":"code","source":["from torchtext import datasets\n","train_data, test_data = datasets.IMDB.splits(text_field=TEXT,label_field=LABEL,)\n","train_data, valid_data = train_data.split(split_ratio= 0.8, random_state=random.seed(SEED))"],"execution_count":0,"outputs":[]},{"metadata":{"id":"36xWKUorYYQw","colab_type":"code","outputId":"e0536fe9-6749-47dd-edef-2f0424f5a461","executionInfo":{"status":"ok","timestamp":1546011931951,"user_tz":-330,"elapsed":19074,"user":{"displayName":"Gaurav Patel","photoUrl":"https://lh3.googleusercontent.com/-OePEQgv0Daw/AAAAAAAAAAI/AAAAAAAAAaI/dyKmC54H6hc/s64/photo.jpg","userId":"02723724286804741615"}},"colab":{"base_uri":"https://localhost:8080/","height":89}},"cell_type":"code","source":["print('Number of training examples: {}'.format(len(train_data)))\n","print('Number of validation examples: {}'.format(len(valid_data)))\n","print(vars(train_data[0]))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Number of training examples: 20000\n","Number of validation examples: 5000\n","{'text': ['wow', 'this', 'movie', 'bored', 'the', 'pants', 'off', 'me', 'when', 'i', 'saw', 'it', 'bland', 'pointless', 'and', 'unmoving', 'apparently', 'ash', 'and', 'co', 'can', 'travel', 'through', 'time', 'with', 'the', 'help', 'of', 'the', 'spirit', 'of', 'the', 'forest', \"'princess\", \"mononoke'\", 'much', 'there', 'they', 'meet', 'a', 'dorky', 'kid', 'named', 'sam', 'and', 'the', 'plot', 'begins', 'so', 'tom', 'ash', 'and', 'huck', 'sam', 'get', 'high', 'with', 'nature', 'become', 'hippies', 'and', 'try', 'to', 'free', 'celebi', 'the', 'spirit', 'from', 'some', 'weirdo', 'hunter', 'guy', 'i', \"don't\", 'even', 'know', 'what', 'else', 'went', 'on', 'it', 'all', 'went', 'by', 'in', 'a', 'blur', \"ash's\", 'friends', 'were', 'hardly', 'in', 'it', 'and', 'all', 'the', 'fight', 'scenes', 'were', 'boring', 'after', 'saving', 'the', 'day', 'ash', 'and', 'his', 'infamous', 'friends', 'must', 'return', 'to', 'their', 'time', 'while', 'watching', 'sam', 'float', 'away', 'with', 'celebi', 'that', 'scene', 'was', 'just', 'creepy', 'oo', 'then', 'after', 'returning', 'to', 'their', 'time', 'ash', 'learns', 'that', 'his', 'new', 'friend', 'is', 'actually', 'his', \"rival's\", 'grandpa', 'and', 'i', 'think', 'that', 'is', 'it', 'pretty', 'retarded', \"isn't\", 'it', 'if', 'you', 'love', 'your', 'children', 'you', 'will', 'not', 'expose', 'them', 'to', 'this', '1', 'out', 'of', '10'], 'label': 'neg'}\n"],"name":"stdout"}]},{"metadata":{"id":"1GMrHuUxXlg-","colab_type":"code","outputId":"1b6e4687-abd9-4000-e73b-d3f4138a0fed","executionInfo":{"status":"ok","timestamp":1546011932567,"user_tz":-330,"elapsed":19531,"user":{"displayName":"Gaurav Patel","photoUrl":"https://lh3.googleusercontent.com/-OePEQgv0Daw/AAAAAAAAAAI/AAAAAAAAAaI/dyKmC54H6hc/s64/photo.jpg","userId":"02723724286804741615"}},"colab":{"base_uri":"https://localhost:8080/","height":379}},"cell_type":"code","source":["train_data_dist = [len(train_data[i].text) for i in range(len(train_data))]\n","valid_data_dist = [len(valid_data[i].text) for i in range(len(valid_data))]\n","test_data_dist = [len(test_data[i].text) for i in range(len(test_data))]\n","\n","sns.set(style='whitegrid')\n","f, axes = plt.subplots(1, 3)\n","\n","sns.distplot(a = train_data_dist,ax=axes[0], axlabel='Train')\n","sns.distplot(a = valid_data_dist,ax=axes[1], axlabel='Validation')\n","sns.distplot(a = test_data_dist,ax=axes[2], axlabel='Test')\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f022f6d3160>"]},"metadata":{"tags":[]},"execution_count":19},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAe0AAAFZCAYAAAC173eYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKM2WLVvYtWsXlZWVrFmzhhUrVuDxeGJe8+1vf5tbb701LW3Xp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yVzjc0wQVsdKqKiRHny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\nCCFEmpCkLYQQQqSJvwDE03d2iIU0YgAAAABJRU5ErkJggg==\n","text/plain":["<matplotlib.figure.Figure at 0x7f022f7af390>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"Zy_iTThEXzjR","colab_type":"code","colab":{}},"cell_type":"code","source":["TEXT.build_vocab(train_data, max_size=25000, vectors=\"glove.6B.100d\")\n","LABEL.build_vocab(train_data)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"mqVeGRU0YSpI","colab_type":"code","colab":{}},"cell_type":"code","source":["import torch.nn as nn\n","import torch.nn.functional as F"],"execution_count":0,"outputs":[]},{"metadata":{"id":"1T8S07AVY-I0","colab_type":"code","colab":{}},"cell_type":"code","source":["\n","class CNN(nn.Module):\n","    def __init__(self,vocab_size, embedding_dim, n_filters, filter_sizes, output_dim, dropout):\n","        super().__init__()\n","        \n","        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n","        self.embedding.weight.requires_grad = False\n","        self.conv_0 = nn.Conv2d(in_channels=1, out_channels=n_filters, kernel_size=(filter_sizes[0],embedding_dim))\n","        self.conv_1 = nn.Conv2d(in_channels=1, out_channels=n_filters, kernel_size=(filter_sizes[1],embedding_dim))\n","        self.conv_2 = nn.Conv2d(in_channels=1, out_channels=n_filters, kernel_size=(filter_sizes[2],embedding_dim))\n","        self.fc = nn.Linear(len(filter_sizes)*n_filters, output_dim)\n","        self.dropout = nn.Dropout(dropout)\n","        \n","    def forward(self, x):\n","        #x = [sen_len, batch_size]\n","        x = x.permute(1,0)\n","        #x = [batch_size, sen_length]\n","        embedded = self.embedding(x)\n","        \n","        # embedded - [batch_size, sent_len, emb_dim]\n","        embedded = embedded.unsqueeze(1)\n","        #embedded = [batch size, 1, sent len, emb dim]\n","       \n","        conved_0 = F.relu(self.conv_0(embedded).squeeze(3))\n","        conved_1 = F.relu(self.conv_1(embedded).squeeze(3))\n","        conved_2 = F.relu(self.conv_2(embedded).squeeze(3))\n","        \n","        #conv_n = [batch size, n_filters, sent len - filter_sizes[n]]\n","        \n","        pooled_0 = F.max_pool1d(conved_0, conved_0.shape[2]).squeeze(2)\n","        pooled_1 = F.max_pool1d(conved_1, conved_1.shape[2]).squeeze(2)\n","        pooled_2 = F.max_pool1d(conved_2, conved_2.shape[2]).squeeze(2)\n","        \n","        #pooled_n = [batch size, n_filters]\n","        \n","        cat = self.dropout(torch.cat((pooled_0, pooled_1, pooled_2), dim=1))\n","\n","        #cat = [batch size, n_filters * len(filter_sizes)]\n","            \n","        return self.fc(cat)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"LqJezieMZSIc","colab_type":"code","colab":{}},"cell_type":"code","source":["INPUT_DIM = len(TEXT.vocab)\n","EMBEDDING_DIM = 100\n","N_FILTERS = 100\n","FILTER_SIZES = [3,4,5]\n","OUTPUT_DIM = 1\n","DROPOUT = 0.5\n","BATCH_SIZE = 128\n","\n","model = CNN(INPUT_DIM, EMBEDDING_DIM, N_FILTERS, FILTER_SIZES, OUTPUT_DIM, DROPOUT)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"6DkugEsaZru9","colab_type":"code","outputId":"f6a6e01e-d6f8-4ee6-ffec-54f6c1d99843","executionInfo":{"status":"ok","timestamp":1546011934704,"user_tz":-330,"elapsed":20648,"user":{"displayName":"Gaurav Patel","photoUrl":"https://lh3.googleusercontent.com/-OePEQgv0Daw/AAAAAAAAAAI/AAAAAAAAAaI/dyKmC54H6hc/s64/photo.jpg","userId":"02723724286804741615"}},"colab":{"base_uri":"https://localhost:8080/","height":139}},"cell_type":"code","source":["pretrained_embeddings  = TEXT.vocab.vectors\n","model.embedding.weight.data.copy_(pretrained_embeddings)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([[ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n","        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n","        [-0.0382, -0.2449,  0.7281,  ..., -0.1459,  0.8278,  0.2706],\n","        ...,\n","        [ 0.3581, -0.3348, -0.4465,  ..., -0.7128, -0.1006, -0.2799],\n","        [ 0.3896,  0.0446, -0.3506,  ...,  0.1727,  0.0145, -0.9884],\n","        [ 0.0994, -0.7076, -0.0949,  ..., -0.0898, -1.1580, -0.1628]])"]},"metadata":{"tags":[]},"execution_count":24}]},{"metadata":{"id":"GCoIXAZfZsj6","colab_type":"code","colab":{}},"cell_type":"code","source":["import torch.optim as optim\n","device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","\n","optimizer = optim.Adam(model.parameters(),lr=0.01)\n","criterion = nn.BCEWithLogitsLoss()\n","model = model.to(device)\n","criterion = criterion.to(device)\n","\n","\n","train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits((train_data,valid_data,test_data), batch_size=BATCH_SIZE,device=device)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"QSzKWu6HZ4iP","colab_type":"code","colab":{}},"cell_type":"code","source":["def binary_accuracy(preds,y):\n","    \n","    rounded_preds = torch.round(torch.sigmoid(preds))\n","    correct = (rounded_preds==y).float()\n","    acc = correct.sum()/len(correct)\n","    return acc\n","\n","\n","def precision_recall_f1(preds, y):\n","    rounded_preds = torch.round(torch.sigmoid(preds))\n","    tp = y*rounded_preds\n","    fp = (y==0).float()*rounded_preds\n","    tn = (y==0).float()*(rounded_preds==0).float()\n","    fn = y*(rounded_preds==0).float()\n","    \n","    return sum(tp), sum(fp), sum(tn), sum(fn)\n","    \n","  \n","  \n","  \n","  \n","def train(model, iterator, optimizer, criterion):\n","        \n","    epoch_loss= 0\n","    epoch_acc = 0\n","    tp = 0\n","    fp = 0\n","    tn = 0\n","    fn = 0\n","        \n","    model.train()\n","        \n","    for batch in iterator:          \n","        optimizer.zero_grad()\n","            \n","        predictions = model(batch.text).squeeze(1)\n","        loss = criterion(predictions, batch.label)\n","        acc = binary_accuracy(predictions, batch.label)\n","        a, b, c, d = precision_recall_f1(predictions, batch.label)\n","       \n","        loss.backward() \n","        optimizer.step()\n","            \n","        epoch_loss += loss.item()\n","        epoch_acc +=acc.item()\n","        tp += a.item()\n","        fp += b.item()\n","        tn += c.item()\n","        fn += d.item()\n","            \n","    return epoch_loss/len(iterator), epoch_acc/len(iterator), tp, fp, tn, fn\n","      \n","      \n","\n","def evaluate(model, iterator, criterion):\n","    \n","    epoch_loss = 0\n","    epoch_acc = 0\n","    tp = 0\n","    fp = 0\n","    tn = 0\n","    fn = 0\n","    \n","    model.eval()\n","    \n","    with torch.no_grad():\n","    \n","        for batch in iterator:\n","            predictions = model(batch.text).squeeze(1)\n","            \n","            loss = criterion(predictions, batch.label)\n","            \n","            acc = binary_accuracy(predictions, batch.label)\n","            a, b, c, d = precision_recall_f1(predictions, batch.label)\n","            \n","            tp += a.item()\n","            fp += b.item()\n","            tn += c.item()\n","            fn += d.item()\n","            epoch_loss += loss.item()\n","            epoch_acc += acc.item()\n","        \n","    return epoch_loss / len(iterator), epoch_acc / len(iterator), tp, fp, tn, fn"],"execution_count":0,"outputs":[]},{"metadata":{"id":"sPxLvKHKabWc","colab_type":"code","outputId":"2a31e49f-3e4c-41e0-817a-9dfef619726c","executionInfo":{"status":"ok","timestamp":1546012023566,"user_tz":-330,"elapsed":85046,"user":{"displayName":"Gaurav Patel","photoUrl":"https://lh3.googleusercontent.com/-OePEQgv0Daw/AAAAAAAAAAI/AAAAAAAAAaI/dyKmC54H6hc/s64/photo.jpg","userId":"02723724286804741615"}},"colab":{"base_uri":"https://localhost:8080/","height":176}},"cell_type":"code","source":["N_EPOCHS = 3\n","\n","for epoch in tqdm(range(N_EPOCHS)):\n","    model.embedding.weight.requires_grad=True\n","    if epoch>0:\n","        lr = 0.001\n","        for param_group in optimizer.param_groups:\n","            param_group['lr'] = lr\n","        model.embedding.weight.requires_grad=False\n","    train_loss, train_acc, train_tp, train_fp, train_tn, train_fn = train(model, train_iterator, optimizer, criterion)\n","    valid_loss, valid_acc, valid_tp, valid_fp, valid_tn, valid_fn = evaluate(model, valid_iterator, criterion)\n","    valid_precision = valid_tp/(valid_tp+valid_fp)\n","    valid_recall = valid_tp/(valid_tp+valid_fn)\n","    valid_f1= (2*valid_precision*valid_recall)/(valid_precision+valid_recall)\n","    print('| Epoch: {} | Train Loss: {} | Train Acc: {} | Val. Loss: {} | Val. Acc: {} | Val. F1 {} |'.format(epoch+1,train_loss, train_acc*100,valid_loss, valid_acc*100, valid_f1))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\n","  0%|          | 0/3 [00:00<?, ?it/s]\u001b[A\n"," 33%|███▎      | 1/3 [00:46<01:33, 46.54s/it]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["| Epoch: 1 | Train Loss: 0.4410485627165266 | Train Acc: 79.61285828025477 | Val. Loss: 0.28408743254840374 | Val. Acc: 88.1640625 | Val. F1 0.877079107505071 |\n"],"name":"stdout"},{"output_type":"stream","text":["\n"," 67%|██████▋   | 2/3 [01:05<00:38, 38.19s/it]\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["| Epoch: 2 | Train Loss: 0.1850227826530007 | Train Acc: 93.33200636942675 | Val. Loss: 0.2833555852994323 | Val. Acc: 88.359375 | Val. F1 0.8807523009203682 |\n"],"name":"stdout"},{"output_type":"stream","text":["\n","100%|██████████| 3/3 [01:23<00:00, 32.35s/it]\u001b[A\n","\u001b[A"],"name":"stderr"},{"output_type":"stream","text":["| Epoch: 3 | Train Loss: 0.16678182337029723 | Train Acc: 93.79478503184714 | Val. Loss: 0.2861498072743416 | Val. Acc: 88.4765625 | Val. F1 0.8824232762056596 |\n"],"name":"stdout"}]},{"metadata":{"id":"y9-o9qULai9B","colab_type":"code","colab":{}},"cell_type":"code","source":["test_loss, test_acc, test_tp, test_fp, test_tn, test_fn= evaluate(model, test_iterator, criterion)\n","test_precision = test_tp/(test_tp+test_fp)\n","test_recall = test_tp/(test_tp+test_fn)\n","test_f1 = (2*test_precision*test_recall)/(test_precision+test_recall)\n","\n","print('| Test Loss: {} | Test Acc: {} | Test F1: {} |'.format(test_loss, test_acc*100, test_f1))"],"execution_count":0,"outputs":[]}]}